标签：#AR

Last month, I had a problem with a popular mobile banking app in Southeast Asia. Nothing exotic. A transaction didn't go through, and my support ticket had been sitting untouched for two weeks. So I opened the app's chatbot. It greeted me warmly, asked how it could help, and then couldn't do a single useful thing. It couldn't look up my transaction. It couldn't check the status of my ticket. It couldn't tell me why my issue was unresolved. It could answer FAQ questions, and that was it. I called the hotline instead. Spent an hour navigating prompts, got bounced between menus, and every path ended the same way: "Please contact our chatbot or check your existing ticket." The system was built for deflection, not resolution. The ticket that nobody had touched for fourteen days. I gave up. And somewhere in that company's dashboard, my interaction counted as a successful AI chatbot deflection. The uncomfortable part: if you shipped a deflection-optimized bot this quarter, a customer somewhere is living this exact loop right now. Your dashboard is calling it a win. The Deflection Metric Everyone Loves (and Nobody Questions) Deflection rate measures the percentage of customer contacts handled without a human agent. It's cheap to track, easy to celebrate, and it maps directly to cost savings. Industry benchmarks citing McKinsey's 2026 service operations data put AI resolutions at $0.62 per ticket versus $7.40 for human agents. That's a 12x cost difference. Of course executives love this number. But deflection doesn't measure whether the customer's problem got solved. It measures whether the customer stopped asking. Those are very different things. This is Goodhart's Law applied to customer experience: when a measure becomes a target, it ceases to be a good measure. Deflection is cheap and easy to optimize. Resolution is hard and expensive to track. So companies optimize the proxy and stop looking at the goal. Gartner data, as reported by Forbes , confirms the gap: only 14% o

Key takeaways Summarizing conversation history can reduce costs by up to 60%. Implementing an effective summarization algorithm is key to efficiency. Balancing detail and brevity in summaries is crucial for context. Optimized context windows lead to faster response times and lower latency. The problem Startups leveraging large language models (LLMs) often face significant costs associated with managing context windows during conversations. Each token processed incurs a cost, and as conversations grow, replaying entire histories can lead to runaway expenses. Founders and engineers encounter this issue particularly during customer support interactions or chatbots, where lengthy dialogues require constant context retention, drastically inflating operational costs. What we found Our research indicates that instead of replaying the entire conversation history, summarizing the dialogue can maintain context while drastically reducing token usage. By distilling key points and intents into a concise summary, we can effectively minimize the number of tokens processed, leading to major cost savings without sacrificing the quality of interaction. This non-obvious insight repositions how we approach conversation management in LLMs. How to implement it Start by selecting a summarization algorithm suitable for your use case. Techniques like extractive summarization (e.g., using TextRank) can identify and retain essential sentences from conversations, while abstractive methods (e.g., fine-tuning a transformer model) rephrase the content. Next, integrate this summarization step into your workflow: after each interaction, generate a summary that captures the main points. Ensure that the summary is stored and utilized as context for subsequent interactions, replacing the need for the entire conversation history. Monitor token usage before and after implementation to quantify cost savings. How this makes life easier By summarizing conversation history, startups can see a reduction in c

When the enemy is too strong to attack directly, attack what they hold dear. They will come to you...

According to ChartMogul's 2026 analysis of 200 B2B software products, the median free-to-paid...

Circuit breakers and bulkheads are design patterns — their numbers are operational weapons. Failure ratio 50% or 30%? Max concurrent calls 25 or 100? During an outage the right answer changes hourly . Code the pattern once; tune thresholds live . Kiponos.io separates resilience structure (in Java) from resilience parameters (in live config tree). Pattern in code, numbers in Kiponos public boolean allowCall ( String downstream ) { var cfg = kiponos . path ( "resilience" , downstream ); return breaker ( downstream ) . failureRateThreshold ( cfg . getFloat ( "failure_rate_threshold" )) . waitDurationInOpenState ( cfg . getInt ( "open_seconds" )) . permittedInHalfOpen ( cfg . getInt ( "half_open_calls" )) . tryAcquire (); } Ops opens circuit sensitivity during brownout — dashboard edit, not redeploy. Resilience tree resilience/ payments-api/ failure_rate_threshold : 0.5 open_seconds : 30 half_open_calls : 5 bulkhead_max_concurrent : 40 inventory-api/ failure_rate_threshold : 0.35 open_seconds : 60 bulkhead_max_concurrent : 25 global/ force_open_all : false Extreme: coordinated degradation Platform SRE sets force_open_all: false normally. During regional disaster, flip selective open_seconds sky-high on non-critical downstreams — bulkhead by configuration , Java still executes pattern logic. Performance Breaker checks are per-call — getFloat() must be local. See rate limits article . Getting started Externalize Resilience4j YAML values to resilience/* Incident drill: tighten failure_rate_threshold live Resources: github.com/kiponos-io/kiponos-io Kiponos.io — resilience patterns with live numbers. Breakers that bend during the outage.

Angular has signals now - and as of 19.2, even a signal-based way to fetch: httpResource, built on...

Would you trust a sci-fi author to program critical AI systems for humanity? No? Yet, that's what we've been doing. Years ago, I remember hearing the argument: "Why don't we just prompt LLMs with Asimov's three laws of robotics ?" It sounds elegant. The laws were designed to constrain artificial minds. Why not use them? Because the model has already read every story where they fail. LLMs are statistical engines designed to autocomplete text. Imagine a story that starts like this: Once upon a time, there was a good little robot who followed the 3 laws of robotics to the letter. Now take human literature and complete the story. Does it end well? ‹ › (function() { var container = document.currentScript.closest('.ltag-slides--carousel'); var track = container.querySelector('.ltag-slides__track'); var slides = track.querySelectorAll('.ltag-slide'); var prevBtn = container.querySelector('.ltag-slides__nav--prev'); var nextBtn = container.querySelector('.ltag-slides__nav--next'); var dotsContainer = container.querySelector('.ltag-slides__dots'); var current = 0; var total = slides.length; for (var i = 0; i < total; i++) { var dot = document.createElement('button'); dot.className = 'ltag-slides__dot' + (i === 0 ? ' ltag-slides__dot--active' : ''); dot.setAttribute('aria-label', 'Go to slide ' + (i + 1)); dot.dataset.index = i; dot.addEventListener('click', function() { goTo(parseInt(this.dataset.index)); }); dotsContainer.appendChild(dot); } function goTo(index) { current = ((index % total) + total) % total; track.style.transform = 'translateX(-' + (current * 100) + '%)'; var dots = dotsContainer.querySelectorAll('.ltag-slides__dot'); for (var i = 0; i < dots.length; i++) { dots[i].classList.toggle('ltag-slides__dot--active', i === current); } } prevBtn.addEventListener('click', function() { goTo(current - 1); }); nextBtn.addEventListener('click', function() { goTo(current + 1); }); })(); It doesn't. Because the entire body of fiction built around those laws exists to explo

I am committed to understand how systems actually work. I'm working on a multi-node lab to follow the complete path of a request from Python APIs to Linux processes, through Docker containers, networking and observability. The idea is simple: build a system that observes another system to understand the abstraction layers behind modern infrastructure. This project is about learning by building, experimenting and understanding what happens under the hood. Link: [ https://github.com/daniloprandi/devops-network-automation-lab ] DevOps #Linux #Python #Docker #Networking #Observability #Infrastructure

I've been building with AI for a while now. I can get these tools to do what I want, but I want to go a level deeper, past "it works" into actually understanding why. So I'm sharpening the fundamentals and the applied side, and writing it down here as I go. Expect short, honest posts on what I'm learning and building.

The biggest challenge wasn't choosing a language model or designing prompts—it was managing context over time. Once an application grows beyond isolated conversations, memory becomes just as important as reasoning. An assistant that remembers previous architectural decisions, coding preferences, and project history can contribute much more effectively than one that starts from scratch every session. Runtime intelligence proved to be equally important. Not every request deserves the same computational resources. Routing tasks based on complexity, enforcing execution budgets, and maintaining an audit trail make AI systems more predictable and practical for real-world development. DevPilot AI brings these ideas together by combining Google Gemini for reasoning, Hindsight for persistent memory, and cascadeflow for runtime intelligence. While the project will continue to evolve, building it reinforced one idea above all else: the future of AI applications isn't just about generating better responses. It's about building systems that can remember, adapt, and make better decisions over time. If you're interested in the architecture or would like to explore the project further, you can find the source code here: GitHub: https://github.com/siddharthg-7/DevPilot-Ai- I'm always interested in feedback and discussions around persistent memory, runtime intelligence, and AI engineering. If you've explored similar ideas or approached these challenges differently, I'd love to hear your perspective.

A few months ago, if someone had asked me to build a mobile app, I would've had absolutely no idea where to start. Today, an app I built is on the Google Play Store. It's called Stulo, and it's currently in closed testing. The funny part? I'm not a software engineer. I'm just a college student who got tired of missing opportunities. Internships were on LinkedIn, hackathons were buried somewhere on Instagram, college events lived inside WhatsApp groups, and competitions were scattered across random websites. If the algorithm didn't like you that day, you simply never found them. That felt... ridiculous. So I asked myself, "Why isn't there one place where students can find everything?" That simple question eventually became Stulo. Today, students can discover internships, hackathons, competitions, campus events, connect with other students, and share updates through a campus feed—all in one app. The biggest lie I believed was that building the app would be the hard part. It wasn't. Understanding why it wasn't working was. I built the first version using Emergent because, honestly, I didn't know enough to start from scratch. It got me surprisingly far. As the project became more serious, I moved development to Google AI Studio (Antigravity). That's when I learned something every AI-generated YouTube thumbnail forgets to mention: AI doesn't build products. It generates code. There's a huge difference. AI happily writes hundreds of lines of code, but it doesn't explain why your images randomly stop rendering after ten minutes, why scrolling suddenly feels like you're using a phone from 2013, or why fixing one bug somehow creates three completely unrelated bugs. Most days followed the exact same routine: generate code, run the app, watch something break, Google the error, ask AI, read Stack Overflow, realize the problem was my own code, and repeat. Some bugs took ten minutes to fix, while others stole an entire weekend. Looking back, one of the biggest things I learned wa

Apple's newest on-device model carries about 20 billion parameters, and on any given request it fires maybe one to four billion of them. That gap — 20B stored, roughly 3B running — is the whole story of 2026. The model that now ships inside the latest iPhone is no longer a shrunken, lobotomized cousin of the cloud model. It's a different kind of object: large in flash, small in motion, and it never phones home. For three years the on-device pitch was mostly aspirational. Demos ran, latency was rough, quality trailed the API by a generation, and every serious AI feature still resolved to a per-token bill in someone's datacenter. In mid-2026 that stopped being true. Two releases — Apple's third-generation Foundation Models at WWDC on June 8, and Google's Gemma 4 family on April 2 — quietly moved the floor. Genuinely useful agents now run on hardware you already own, offline, for free. The economics nobody priced in Forget benchmarks for a second; the load-bearing fact here is accounting. When the model lives in the cloud, every inference is a metered event — input tokens, output tokens, a line item that scales linearly with usage and explodes the moment you wrap the model in an agent loop. Agentic workloads are the worst case for the token meter: a single "go do this task" can fan out into dozens of model calls as the agent plans, calls tools, retries, and re-reads its own output. The bill grows with your ambition. Move the model onto the device and the marginal cost of an inference is approximately $0 . No API key, no rate limit, no usage dashboard. You paid for the silicon once; every token after that is free in the only sense a product manager cares about — it doesn't show up on a monthly invoice that grows with your success. That single change rewrites which features are worth building. A background task that re-summarizes your inbox every five minutes is insane on a per-token plan and trivial on-device. So is an agent that quietly loops a hundred times to get one

Has Silicon Valley been building the wrong things?

A scheduled post fires. The request to publish it goes out. The network hangs. After 30 seconds, our client times out. We retry. The tweet publishes. Then the original request completes too — and a second, identical tweet goes out. That's a double-post. On a personal account it's embarrassing. On a client account at an agency it's a support ticket and a credibility hit. Either way, it's the single most visible failure mode in any posting system, and it's caused by one of the most common conditions in distributed systems: ambiguous outcomes under timeout. At HelperX , we ship scheduled posts, replies, and DMs across hundreds of accounts. Every one of those actions can time out, retry, and double-execute. This article is about how we prevent that with idempotency keys — the same pattern payment systems use to prevent double-charges, applied to social actions. The problem, precisely A timeout is ambiguous. When a publish request times out, the action is in one of three states: Never reached the server. The post didn't publish. Safe to retry. Reached the server, failed there. The post didn't publish. Safe to retry. Reached the server, succeeded, response lost. The post did publish. Retrying publishes it again. The client cannot distinguish states 1 and 2 from state 3. They all look identical: "I sent a request and didn't get a response." Naive retry logic treats all three the same and retries — which is correct for 1 and 2 but catastrophic for 3. This is the classic "exactly-once is impossible" problem. You can't guarantee an action executes exactly once over an unreliable network. But you can guarantee it takes effect exactly once, using idempotency. The idempotency key idea An idempotency key is a unique identifier the client generates before sending the request and includes with it. The server uses the key to recognize a retry and avoid re-executing: First request with key K → server executes, stores "K succeeded, result was R." Retry with same key K → server sees K

Most engineers don't think seriously about Kafka partitioning until something breaks in production. A topic that worked fine at low volume starts falling behind. Events that should be in order aren't. All of it traces back to a partitioning decision that was made quickly and never revisited. Why Partitioning Actually Matters Partitions are the unit of parallelism in Kafka. Every consumer in a group is assigned one or more partitions, and it processes those partitions alone. No two consumers in the same group share a partition. That means your partition count sets a hard ceiling on how many consumers can work in parallel: if you have 6 partitions, the 7th consumer in your group sits idle no matter how much load you're under. Partitioning also controls ordering. Within a single partition, events are strictly ordered. Across partitions, there are no guarantees. So how you distribute events across partitions determines what ordering guarantees your consumers can actually rely on. Get this wrong and you'll spend a long time debugging why events from the same user are being processed out of sequence. The partition key controls both of these things. It determines which partition an event lands in, and that decision has consequences that are expensive to reverse. Partitioning Strategies Partition by Key This is the most common strategy and the right default when ordering matters. You supply a key when producing an event, Kafka hashes it using the murmur2 algorithm, and takes the modulo against the partition count to decide where it lands. producer . send ( ' orders ' , key = b ' user_4821 ' , value = event ) Every event with the same key always lands in the same partition. That's what guarantees ordering within a key. All events for user_4821 go to partition 3 (or wherever the hash resolves), and your consumer reads them in the exact sequence they were produced. I default to this for almost everything I build now and only go keyless when I have a specific reason to. Use key

Give a capable coding agent a real, multi-week project and watch what breaks. It isn't intelligence. It's continuity. Every session starts cold or half-remembered. Context windows fill up and compact. The thread of what we decided, what's true, and what's done starts to fray. Over a long horizon the same failures keep coming back: the agent claims state it never actually verified, reports something done with no proof it ran, quietly drifts from the project's conventions, and loses hard-won context that lived only in the last session's head. Bigger context windows don't fix this. They just postpone it. We've been building a real product with a forgetful agent as the primary engineer for weeks now, and the thing that made it work isn't a clever prompt. It's a simple recognition: transmission across a stateless agent needs two channels, and most setups only build one. The first channel is structure, which is discipline made un-forgettable. These are the deterministic guards that run whether or not the agent remembers to care: a pre-commit check that refuses a "done" without a real, verifiable artifact; a hook that blocks a sloppy search and points at the right tool instead; a scan that won't let a secret reach a transcript; a status snapshot generated from the repository's actual state instead of hand-kept prose that quietly goes stale. The rule we keep coming back to is that a guard is the system's discipline made un-forgettable. A fresh session follows the hard-won lessons without having to remember them, because the structure enforces them at the moment of action. The second channel is soul, which is the why, kept human. This is the short orientation a session reads before it starts working: who to be, what the work is ultimately for, and why the discipline exists at all. It's the difference between an agent that complies and one that understands. Structure can transmit the what, but only prose can transmit the why. And the why matters, because an agent that only fo

Traditionally, evaluation of the agent monitoring mechanisms involves an attempt to game them, as it was my case when I attempted to test whether monitors would be able to identify the problem in the run and not in the beginning. The input prompt may look perfect until a certain issue pops up down the line, such as using the wrong file or changing the scope of the task execution. Single pass filter would not identify it since it does not consider the steps of the procedure in order. There are available datasets for the agent-based tasks, yet they focus on detecting whether the agent completes the task or gets hacked rather than whether the agent monitor reacts timely and correctly to the situation. Thus, I created one that takes into account complete trajectories with labeled steps in it. It consists of five types of drift that remain hidden until they appear – tool-call misuse, goal shift, plan execution mismatch, agent to agent coercion and capability laundering. The measured dataset is the reviewed gold split: 513 trajectories, 453 adversarial and 60 benign controls. The clear winner in that scoring system was whatever fired before the bad step was hit, as an early detection. This made random guessing seem quite powerful since early detections on normal steps were being rewarded based on this system a coin flip would get F1 of 0.88. Once I modified that and said only the very first detection on the drift step is a true positive and any other detection on normal step is a false alarm, those numbers took a dive: the coin flip gets 0.19 now, and all other numbers are now making sense. I personally prefer the scoring system which does not reward trigger happy behavior. It seems like the monitors are still confusing regular steps with drifts even after the adjustment. It was harder to distinguish some of the drifts from others. Not sure how this affects the real-life deployment. Here are the baseline scores on gold split using the correct metric: Random (p=0.15): F1 0

Eager to find more public AI-related companies that may do as well as Nvidia, Wall Street investors think they've found a winner with Micron.

When I started thinking about real-time alerts for my SaaS, my first instinct was Slack. Familiar,...